Predicting the distribution of invasive weeds under climate change is important for the early identification of areas that are susceptible to invasion and for the adoption of the best preventive measures. Here, we predicted the habitat suitability of 16 invasive weeds in response to climate change and land cover changes in South Korea using a maximum entropy modeling approach. Based on the predictions of the model, climate change is likely to increase habitat suitability. Currently, the area of moderately suitable and highly suitable habitats is estimated to be 8877.46 km², and 990.29 km², respectively, and these areas are expected to increase up to 496.52% by 2050 and 1439.65% by 2070 under the representative concentration pathways 4.5 scenario across the country. Although habitat suitability was estimated to be highest in the southern regions (<36° latitude), the central and northern regions are also predicted to have substantial increases in suitable habitat areas. Our study revealed that climate change would exacerbate the threat of northward weed invasions by shifting the climatic barriers of invasive weeds from the southern region. Thus, it is essential to initiate control and management strategies in the southern region to prevent further invasions into new areas.

Invasion by non-native species due to human activities is a major threat to biodiversity. The niche hypothesis for invasive species that rapidly disperse and disturb ecosystems is easily discarded owing to eradication activities or unsaturated dispersal. Here, we used spatial and non-spatial models to model the distribution of two invasive plant species (Ambrosia artemisiifolia and Ambrosia trifida), which are widely distributed, but are also being actively eradicated. Regression kriging (RK) and Maxent were used to predict the spatial distribution of the two plant species having eradication targets for decades in South Korea. In total, 1,478 presence/absence data points in the Seoul metropolitan area (∼11,000 km² in northeastern South Korea) were used. For regression kriging, the presence/absence data were first fitted with environmental covariates using a generalized linear model (GLM), and then the residuals of the GLM were modeled using ordinary kriging. The residuals of GLM showed significant spatial autocorrelation. The spatial autocorrelation was modeled using kriging. Regression kriging, which considers the spatial structure of data, yielded area under the receiver operating curve values of 0.785 and 0.775 for A. artemisiifolia and A. trifida, respectively; however, the values of Maxent, a non-spatial model, were 0.619 and 0.622, respectively. Thus, regression kriging was advantageous as it considers the spatial autocorrelation of the data. However, species distribution modeling encounters difficulties when the current species distribution does not reflect optimal habitat conditions (the niche habitat preferences) or when colonization is disturbed by artificial interference (e.g., removal activity). This greatly reduces the predictive power of the model if the model is based solely on the niche hypotheses that do not reflect reality. Managers can take advantage of regression modeling when modeling species distributions under conditions unfavorable to the niche hypothesis.

The nutria, (Myocastor coypus), is a semiaquatic rodent native to the subtropical and temperate regions of South America. The species was introduced to South Korea for meat and fur production purposes and a wild population has become established. The species subsequently invaded aquatic ecosystems and destroyed aquatic vegetation and cultivated crops. Thus, it is essential to understand their current distribution and future range expansion for effective control and eradication strategies to reduce the risk of colonization into new regions. In this study, we used niche modeling procedure to identify potentially suitable habitats for M. coypus under current and future predicted climate change using the maximum entropy algorithm. We found that the main habitat area of M. coypus is expected to expand under a warming climate from ~ 4069 km² in the southern and southeastern regions of South Korea, to the northern border of the country, with estimated ranges of 21,744 km², 55,859 km², and 64,937 km² by 2030, 2050, and 2070, respectively. The findings of the present study assist in identifying the future distribution and potential dispersion routes of M. coypus in South Korea, which is important for informing the government regarding essential management actions plans at regional and local scales.

Climate change is an inevitable and urgent issue in the current world. African swine fever virus (ASFV) is a re-emerging viral animal disease. This study investigates the quantitative association between climate change and the potential spread of ASFV to a global extent. ASFV in wild boar outbreak locations recorded from 1 January 2019 to 29 July 2022 were sampled and investigated using the ecological distribution tool, the Maxent model, with WorldClim bioclimatic data as the predictor variables. The future impacts of climate change on ASFV distribution based on the model were scoped with Representative Concentration Pathways (RCP 2.6, 4.5, 6.0, and 8.5) scenarios of Coupled Model Intercomparison Project 5 (CMIP5) bioclimatic data for 2050 and 2070. The results show that precipitation of the driest month (Bio14) was the highest contributor, and annual mean temperature (Bio1) was obtained as the highest permutation importance variable on the spread of ASFV. Based on the analyzed scenarios, we found that the future climate is favourable for ASFV disease; only quantitative ratios are different and directly associated with climate change. The current study could be a reference material for wildlife health management, climate change issues, and World Health Organization sustainability goal 13: climate action.

The emergence and spread of antibiotic-resistant Aeromonas spp. is a serious public and animal health concern. Wild animals serve as reservoirs, vectors, and sentinels of these bacteria and can facilitate their transmission to humans and livestock. The nutria (Myocastor coypus), a semi-aquatic rodent, currently is globally considered an invasive alien species that has harmful impacts on natural ecosystems and carries various zoonotic aquatic pathogens. This study aimed to determine the prevalence of antibiotic-resistant zoonotic Aeromonas spp. in wild invasive nutrias captured in Korea during governmental eradication program. Three potential zoonotic Aeromonas spp. (A. hydrophila, A. caviae, and A. dhakensis) were identified among isolates from nutria. Some strains showed unexpected resistance to fluoroquinolones, third-generation cephalosporins, and carbapenems. In carbapenem-resistant isolates, the cphA gene, which is related to intrinsic resistance of Aeromonas to carbapenems, was identified, and phylogenetic analysis based on this gene revealed the presence of two major groups represented by A. hydrophila (including A. dhakensis) and other Aeromonas spp. These results indicate that wild nutrias in Korea are a potential reservoir of zoonotic and antibiotic-resistant Aeromonas spp. that can cause infection and treatment failure in humans. Thus, measures to prevent contact of wild nutrias with livestock and humans are needed.

Entropy is widely used for measuring the degree of urban sprawl. However, despite the intense use of the entropy concept in urban sprawl, entropy’s spatial context has been largely ignored. In this study, we analyzed urban sprawl in Changwon and Gimhae cities, as they shared a common boundary but differed in their population growth and urban expansion. The land cover type, “urban and dry area,” was used to identify urban areas in the two cities, and a land cover map showed the areas of expansion in the 1980s, 1990s, 2000s, and 2010s. Different zoning schemes, namely concentric rings and regular partitioning, were applied. Shannon’s and Batty’s spatial entropy indices were used to measure urban sprawl. The results showed that concentric ring zoning was not suitable for measuring urban sprawl in a decentralized and polycentric city. Batty’s spatial entropy was less affected by the zoning scheme used and reflected the pattern of urban expansion more accurately. Urban sprawl, a phenomenon occurring within a spatial context, can be better understood by measuring spatial entropy with appropriate zoning schemes.